'''simple docstring'''






import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_UpperCAmelCase    :     Optional[int]         =       logging.get_logger(__name__)

enable_full_determinism()







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   UNetaDModel
   __lowerCAmelCase         =   "sample"
   @property
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Any:
        _A    		=  4
        _A    		=  3
        _A    		=  (32, 32)

        _A    		=  floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCamelCase__     )
        _A    		=  torch.tensor([10]     ).to(UpperCamelCase__     )

        return {"sample": noise, "timestep": time_step}
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Any:
        return (3, 32, 32)
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			List[Any]:
        return (3, 32, 32)

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			List[str]:
        _A    		=  {
            'block_out_channels': (32, 64),
            'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
            'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
            'attention_head_dim': 3,
            'out_channels': 3,
            'in_channels': 3,
            'layers_per_block': 2,
            'sample_size': 32,
        }
        _A    		=  self.dummy_input
        return init_dict, inputs_dict







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   UNetaDModel
   __lowerCAmelCase         =   "sample"
   @property
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[int]:
        _A    		=  4
        _A    		=  4
        _A    		=  (32, 32)

        _A    		=  floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCamelCase__     )
        _A    		=  torch.tensor([10]     ).to(UpperCamelCase__     )

        return {"sample": noise, "timestep": time_step}
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Tuple:
        return (4, 32, 32)
   @property
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        return (4, 32, 32)
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			List[Any]:
        _A    		=  {
            'sample_size': 32,
            'in_channels': 4,
            'out_channels': 4,
            'layers_per_block': 2,
            'block_out_channels': (32, 64),
            'attention_head_dim': 32,
            'down_block_types': ('DownBlock2D', 'DownBlock2D'),
            'up_block_types': ('UpBlock2D', 'UpBlock2D'),
        }
        _A    		=  self.dummy_input
        return init_dict, inputs_dict
   def 	__UpperCAmelCase   (      self      :			Optional[Any]     )  ->			Optional[int]:
        _A		,       _A    		=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=UpperCamelCase__     )

        self.assertIsNotNone(UpperCamelCase__     )
        self.assertEqual(len(loading_info['missing_keys']     ), 0     )

        model.to(UpperCamelCase__     )
        _A    		=  model(**self.dummy_input     ).sample

        assert image is not None, "Make sure output is not None"
   @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU'     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[Any]:
        _A		,       _A    		=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=UpperCamelCase__     )
        model.to(UpperCamelCase__     )
        _A    		=  model(**self.dummy_input     ).sample

        assert image is not None, "Make sure output is not None"
   @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU'     )
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			Union[str, Any]:
        # by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
        _A		,       _A    		=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=UpperCamelCase__     )
        model_accelerate.to(UpperCamelCase__     )
        model_accelerate.eval()

        _A    		=  torch.randn(
            1, model_accelerate.config.in_channels, model_accelerate.config.sample_size, model_accelerate.config.sample_size, generator=torch.manual_seed(0     ), )
        _A    		=  noise.to(UpperCamelCase__     )
        _A    		=  torch.tensor([10] * noise.shape[0]     ).to(UpperCamelCase__     )

        _A    		=  model_accelerate(UpperCamelCase__, UpperCamelCase__     )['sample']

        # two models don't need to stay in the device at the same time
        del model_accelerate
        torch.cuda.empty_cache()
        gc.collect()

        _A		,       _A    		=  UNetaDModel.from_pretrained(
            'fusing/unet-ldm-dummy-update', output_loading_info=UpperCamelCase__, low_cpu_mem_usage=UpperCamelCase__     )
        model_normal_load.to(UpperCamelCase__     )
        model_normal_load.eval()
        _A    		=  model_normal_load(UpperCamelCase__, UpperCamelCase__     )['sample']

        assert torch_all_close(UpperCamelCase__, UpperCamelCase__, rtol=1e-3     )

   def 	__UpperCAmelCase   (      self      :			List[Any]     )  ->			Any:
        _A    		=  UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'     )
        model.eval()
        model.to(UpperCamelCase__     )

        _A    		=  torch.randn(
            1, model.config.in_channels, model.config.sample_size, model.config.sample_size, generator=torch.manual_seed(0     ), )
        _A    		=  noise.to(UpperCamelCase__     )
        _A    		=  torch.tensor([10] * noise.shape[0]     ).to(UpperCamelCase__     )

        with torch.no_grad():
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     ).sample

        _A    		=  output[0, -1, -3:, -3:].flatten().cpu()
        # fmt: off
        _A    		=  torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]     )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__, UpperCamelCase__, rtol=1e-3     )     )







class lowercase_       (		_UpperCamelCase    ,    _UpperCamelCase    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   UNetaDModel
   __lowerCAmelCase         =   "sample"
   @property
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			List[Any]=(32, 32)     )  ->			Optional[Any]:
        _A    		=  4
        _A    		=  3

        _A    		=  floats_tensor((batch_size, num_channels) + sizes     ).to(UpperCamelCase__     )
        _A    		=  torch.tensor(batch_size * [10]     ).to(dtype=torch.intaa, device=UpperCamelCase__     )

        return {"sample": noise, "timestep": time_step}
   @property
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			int:
        return (3, 32, 32)
   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Tuple:
        return (3, 32, 32)
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Tuple:
        _A    		=  {
            'block_out_channels': [32, 64, 64, 64],
            'in_channels': 3,
            'layers_per_block': 1,
            'out_channels': 3,
            'time_embedding_type': 'fourier',
            'norm_eps': 1e-6,
            'mid_block_scale_factor': math.sqrt(2.0     ),
            'norm_num_groups': None,
            'down_block_types': [
                'SkipDownBlock2D',
                'AttnSkipDownBlock2D',
                'SkipDownBlock2D',
                'SkipDownBlock2D',
            ],
            'up_block_types': [
                'SkipUpBlock2D',
                'SkipUpBlock2D',
                'AttnSkipUpBlock2D',
                'SkipUpBlock2D',
            ],
        }
        _A    		=  self.dummy_input
        return init_dict, inputs_dict
   @slow
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			List[Any]:
        _A		,       _A    		=  UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256', output_loading_info=UpperCamelCase__     )
        self.assertIsNotNone(UpperCamelCase__     )
        self.assertEqual(len(loading_info['missing_keys']     ), 0     )

        model.to(UpperCamelCase__     )
        _A    		=  self.dummy_input
        _A    		=  floats_tensor((4, 3) + (2_56, 2_56)     ).to(UpperCamelCase__     )
        _A    		=  noise
        _A    		=  model(**UpperCamelCase__     )

        assert image is not None, "Make sure output is not None"
   @slow
   def 	__UpperCAmelCase   (      self      :			str     )  ->			Optional[Any]:
        _A    		=  UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'     )
        model.to(UpperCamelCase__     )

        _A    		=  4
        _A    		=  3
        _A    		=  (2_56, 2_56)

        _A    		=  torch.ones((batch_size, num_channels) + sizes     ).to(UpperCamelCase__     )
        _A    		=  torch.tensor(batch_size * [1e-4]     ).to(UpperCamelCase__     )

        with torch.no_grad():
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     ).sample

        _A    		=  output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        _A    		=  torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]     )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__, UpperCamelCase__, rtol=1e-2     )     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'     )
        model.to(UpperCamelCase__     )

        _A    		=  4
        _A    		=  3
        _A    		=  (32, 32)

        _A    		=  torch.ones((batch_size, num_channels) + sizes     ).to(UpperCamelCase__     )
        _A    		=  torch.tensor(batch_size * [1e-4]     ).to(UpperCamelCase__     )

        with torch.no_grad():
             _A    		=  model(UpperCamelCase__, UpperCamelCase__     ).sample

        _A    		=  output[0, -3:, -3:, -1].flatten().cpu()
        # fmt: off
        _A    		=  torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]     )
        # fmt: on

        self.assertTrue(torch_all_close(UpperCamelCase__, UpperCamelCase__, rtol=1e-2     )     )

   def 	__UpperCAmelCase   (      self      :			str     )  ->			Tuple:
        # not required for this model
        pass









"""simple docstring"""




def        a__					(							snake_case__							)		->   list:

			if len(snake_case__							) <= 1:
						return [tuple(snake_case__							)]

			lowerCamelCase      =   []

			def generate(snake_case__			,					snake_case__							):
						lowerCamelCase      =   [0] * n
						res.append(tuple(snake_case__							)							)

						lowerCamelCase      =   0
						while i < n:
									if c[i] < i:
												if i % 2 == 0:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[0]
												else:
															lowerCamelCase ,  lowerCamelCase      =   arr[i], arr[c[i]]
												res.append(tuple(snake_case__							)							)
												c[i] += 1
												lowerCamelCase      =   0
									else:
												lowerCamelCase      =   0
												i += 1

			generate(len(snake_case__							)			,					snake_case__							)
			return res


if __name__ == "__main__":
					lowerCAmelCase				:     str					=				input("""Enter numbers separated by a comma:\n""").strip()
					lowerCAmelCase				:     List[Any]					=				[int(item) for item in user_input.split(""",""")]
					print(heaps(arr))



